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Big Data and the
Quantified Self

October 28, 2013
National Consumer Res Ctr, Helsinki, Finland
Slides: http://slideshare.net/LaBlogga

Melanie Swan
MS Futures Group
+1-650-681-9482
@LaBlogga, @DIYgenomics
www.MelanieSwan.com
m@melanieswan.com
http://www.youtube.com/TechnologyPhilosophe
About Melanie Swan






Founder DIYgenomics, science and
technology innovator and philosopher
Singularity University Instructor, IEET
Affiliate Scholar, EDGE Contributor
Education: MBA Finance, Wharton; BA
French/Economics, Georgetown Univ
Work experience: Fidelity, JP Morgan, iPass,
RHK/Ovum, Arthur Andersen
Sample publications:



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

Swan, M. Crowdsourced Health Research Studies: An Important Emerging Complement to Clinical Trials in the Public
Health Research Ecosystem. J Med Internet Res 2012, Mar;14(2):e46.
Swan, M. Scaling crowdsourced health studies: the emergence of a new form of contract research organization.
Personalized Medicine 2012, Mar;9(2):223-234.
Swan, M. Steady advance of stem cell therapies. Rejuvenation Res 2011, Dec;14(6):699-704.
Swan, M., Hathaway, K., Hogg, C., McCauley, R., Vollrath, A. Citizen science genomics as a model for crowdsourced
preventive medicine research. J Participat Med 2010, Dec 23; 2:e20.
Swan, M. Multigenic Condition Risk Assessment in Direct-to-Consumer Genomic Services. Genet Med 2010,
May;12(5):279-88.
Swan, M. Emerging patient-driven health care models: an examination of health social networks, consumer personalized
medicine and quantified self-tracking. Int J Environ Res Public Health 2009, 2, 492-525.

October 28, 2013
QS Big Data

Source: http://melanieswan.com/publications.htm

2
Conceptualizing Big Data Categories
Personal Data
Tension: Individual vs Institution

Group Data
Sense of data belonging to a group

October 28, 2013
QS Big Data

3
Agenda


Personal Data






Group Data




Quantified Self
Quantified Self and Big Data
Advanced QS Concepts
Urban Data

Conclusion

October 28, 2013
QS Big Data

4
What is the Quantified Self?


Individual engaged in the selftracking of any kind of biological,
physical, behavioral, or
environmental information



Data acquisition through
technology: wearable sensors,
mobile apps, software interfaces,
and online communities



Proactive stance: obtain and act
on information

October 28, 2013
QS Big Data

Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June
2013, 1(2): 85-99.

5
QS Sensor Mania! Wearable Electronics

Smartphone, Fitbit, Smartwatch (Pebble), Electronic T-shirt (Carre)

Smartring (ElectricFoxy), Electronic tattoos (mc10), $1 blood API
(Sano Intelligence), Continuous Monitors (Medtronic)

October 28, 2013
QS Big Data

Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified
Self 2.0. J Sens Actuator Netw 2012.

6
Wearable Personal Information Ecosystem
Smart Gadgetry Creates Continuous Personal Information Climate
New Wearable Categories:
Smartwatch and AR/Glass

Smartphone

PC/Tablet/Cloud

October 28, 2013
QS Big Data

AR = Augmented Reality

7
Next-gen Mini: BioSensor Electronic Tattoos
Wearable Electronics: Detect External BioChemical
Threats and Track Internal Vital Signs

Electrochemical Sensors

Chemical Sensors
October 28, 2013
QS Big Data

Disposable Electronics

Source: http://www.jacobsschool.ucsd.edu/pulse/winter2013/page3.shtml#tattoos

Tactile Intelligence:
Haptic Data Glove
8
Quantified Self Worldwide Community



Goal: personalized knowledge through
quantified self-tracking
‘Show n tell’ meetups


What did you do? How did you do it? What
did you learn?
Videos, Conferences, Meetup Groups

October 28, 2013
QS Big Data

Source: Swan, M. Overview of Crowdsourced Health Research Studies. 2012.

9
October 28, 2013
QS Big Data

Source: http://www.meetup.com/Quantified-Self-Biohacking-Finland/

10
Quantified Self Project Examples


Food consumption (1 yr)1 and the Butter Mind study2
Study



Low-cost home-administered blood, urine, saliva tests



Cholestech LDX
home cholesterol test
October 28, 2013
QS Big Data

1
2

OrSense continuous non-invasive
glucose monitoring

Source: http://flowingdata.com/2011/06/29/a-year-of-food-consumption-visualized
Source: http://quantifiedself.com/2011/01/results-of-the-buttermind-experiment

ZRT Labs dried
blood spot tests
11
Quantified Self Measurements…


Physical Activities




Diet and Nutrition






Location, architecture, weather, noise, pollution, clutter, light, season

Situational Variables




Mood, happiness, irritation, emotion, anxiety, esteem, depression, confidence
IQ, alertness, focus, selective/sustained/divided attention, reaction, memory,
verbal fluency, patience, creativity, reasoning, psychomotor vigilance

Environmental Variables




Calories consumed, carbs, fat, protein, specific ingredients, glycemic index,
satiety, portions, supplement doses, tastiness, cost, location

Psychological, Mental, and Cognitive States and Traits




Miles, steps, calories, repetitions, sets, METs1

Context, situation, gratification of situation, time of day, day of week

Social Variables


October 28, 2013
QS Big Data

Influence, trust, charisma, karma, current role/status in the group or social network
METs = Metabolic equivalents Source: http://measuredme.com/2012/10/building-thatperfect-quantified-self-app-notes-to-developers-and-qs-community-html/
1

12
The Quantified Self is Mainstream


Self-tracking statistics







60% US adults track weight, diet, or exercise
33% US adults monitor blood sugar, blood pressure,
headaches, or sleep patterns
9% receive text message health alerts
40,000 smartphone health applications

QS thought leadership




Press : BBC, Forbes, and Vanity Fair
Electronics show focus at CES 2013
Health 2.0: “500+ companies making
self-management tools; VC funding up 20%”

October 28, 2013
QS Big Data

Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data
June 2013, 1(2): 85-99.

13
Hype Curves per Google Trends

2011

October 28, 2013
QS Big Data

2013

2011

2013

14
QS Experimentation Motivation and Features


DIYgenomics QS Study (n=37)



Desired outcome: optimality and
improvement (vs pathology resolution)






Personalized intervention for depression,
low energy, sleep quality, productivity, and
cognitive alertness

Rapid experimental iteration through
solutions and kinds of solutions
Resolution point found within weeks
Pragmatic problem-solving focus, little
introspection

October 28, 2013
QS Big Data

Source: DIYgenomics Knowledge Generation through Self-Experimentation Study
http://genomera.com/studies/knowledge-generation-through-self-experimentation

15
History of the Quantified Self




Sanctorius of Padua 16th c: energy
expenditure in living systems; 30
years of QS weight/food data
QS Philosophers





Epicureans, Heidegger, Foucault): ‘care
of the self’
‘Self’: recent concept of modernity

QS: contemporary formalization using
measurement, science, and
technology to bring order and control
to the natural world, including the
human body

October 28, 2013
QS Big Data

Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June
2013, 1(2): 85-99.

16
Sensor Mania!

October 28, 2013
QS Big Data

Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the
Quantified Self 2.0. J Sens Actuator Netw 2012.

17
Wireless Internet-of-Things (IOT)

Image credit: Cisco

October 28, 2013
QS Big Data

Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified Self 2.0.
J Sens Actuator Netw 2012.

18
6 bn Current IOT devices to double by 2016

October 28, 2013
QS Big Data

Source: http://www.businessinsider.com/growth-in-the-internet-of-things-2013-10?IR=T

19
IOT World of Smart Matter


IOT Definition: digital networks of
physical objects linked by the Internet
that interact through web services



Usual gadgetry (e.g.; smartphones,
tablets) and now everyday objects:
cars, food, clothing, appliances,
materials, parts, buildings, roads



Embedded microprocessors in 5%
human-constructed objects (2012)1

October 28, 2013
QS Big Data

Source: Vinge, V. Who’s Afraid of First Movers? The Singularity Summit 2012.
http://singularitysummit.com/schedule
1

20
IOT Contributing to Explosion of Big Data


Big Data: data sets too large and
complex to process with on-hand
database management tools



Examples





Walmart : 1 million transactions/hr
transmitted to 3 PB database
BBC: 7 PB video served/month from
100 PB physical disk space

Structured and unstructured data
(not pre-defined)

October 28, 2013
QS Big Data

Source: http://en.wikipedia.org/wiki/Big_data, http://wikibon.org/blog/big-data-statistics

21
Defining Trend of Current Era: Big Data




Annual data creation on the order of zetabytes
90% of the world’s data created in the last 2 years
Fastest growing segment: human biology-related data

2 year doubling cycle

October 28, 2013
QS Big Data

Source: Mary Meeker, Internet Trends, http://www.kpcb.com/insights/2013-internet-trends
http://www.intel.com/content/dam/www/public/us/en/documents/white-papers/healthcare-leveraging-big-data-paper.pdf

22
QS is inherently a Big Data problem



Data collection, processing, analysis
Cloud computing for consumer processing





Local computing tools are not available to store,
query, and manipulate QS data sets
Cloud-based analysis: Predictive modeling,
natural-language processing, machine learning
algorithms over very-large data sets of
heterogeneous data

Rapid growth in QS data sets



Manually-tracked ‘small data’ is now
automatically-collected ‘big data’
Examples: heart rate monitor data - 250
samples/second (9 GB/person/month);
personal health ‘omics’ files

October 28, 2013
QS Big Data

Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data
June 2013, 1(2): 85-99.

23
QS Big Data: Personal Health ‘Omics’
DNA:
SNP mutations

DNA: Structural
variation

RNA expression
profiling

Health 2.0:
Personal Health
Informatics

Proteomics

Microbiomics

Epigenetics
Metabolomics

October 28, 2013
QS Big Data

Source: Academic papers re: integrated health data streams: Auffray C, et al. Looking back at genomic medicine in 2011. Genome Med. 2012
Jan 30;4(1):9. Chen R et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell. 2012 Mar 16;148(6):1293-307.

24
Big Data: Integrated QS Data Streams
Omics Data Streams
Genome
SNP mutations
Structural variation
Epigenetics

Microbiome

Traditional Data Streams
Personal and Family
Health History

Proteome

Self-reported data:
health, exercise,
food, mood
journals, etc.

Prescription History

Transcriptome
Metabolome

Quantified Self Data
Streams

Mobile App Data
Lab Tests: History
and Current

Demographic Data

Quantified Self
Device Data

Standardized
Instrument Response

Biosensor Data
Objective Metrics

Diseasome
Environmentome
October 28, 2013
QS Big Data

Swan, M. Health 2050: The Realization of Personalized Medicine through Crowdsourcing, the Quantified Self, and the Participatory
Biocitizen. J Pers Med 2012, 2(3), 93-118.

Legend: Consumer-available

25
APIs and Multi-QS Data Stream Integration

October 28, 2013
QS Big Data

26
Fluxstream Unified QS Dashboard

October 28, 2013
QS Big Data

Source: http://johnfass.wordpress.com/2012/09/06/bodytrackfluxtream/

27
Sen.se Integrated QS Dashboard


‘Mulitviz’ display: investigate correlation between coffee
consumption, social interaction, and mood

October 28, 2013
QS Big Data

Source: http://blog.sen.se/post/19174708614/mashups-turning-your-data-intosomething-useable-and

28
Wholly different concept and relation to data



Formerly everything signal, now 99% noise
Medium of big data opens up new methods:



Exception, characterization, variability, pattern recognition,
correlation, prediction, early warnings



Allows attitudinal shift to active from reactive



Two-way communication: translate biometric variability in the
personal informatics climate to real-time recommendations
Example: degradation in sleep quality and hemoglobin A1C levels
predict diabetes onset by 10 years1



October 28, 2013
QS Big Data

Source: Heianza et al. High normal HbA(1c) levels were associated with
impaired insulin secretion. Diabet Med 2012. 29:1285-1290.
1

29
Big Data opens up new Methods



Google: large corpora and simple algorithms
Foundational characterization (previously unavailable)


Longitudinal baseline measures of internal and external daily
rhythms, normal deviation patterns, contingency adjustments,
anomaly, and emergent phenomena



New kinds of Pattern Recognition (different structures)



Analyze data in multiple paradigms: time, frequency, episode, cycle,
and systemic variables
New trends, cyclicality, episodic triggers, and other elements that
are not clear in traditional time-linear data





Multi-disciplinarity


Turbulence, topology, chaos, complexity, etc. models

October 28, 2013
QS Big Data

Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data
June 2013, 1(2): 85-99.

30
Opportunity: QS Data Commons


Common repository for personal informatics
data streams




Fitbit, Jawbone UP, Nike, Withings, myZeo,
23andMe, Glass, Pebble, Basis, BodyMedia

Architecting consumer-friendly models


Open-access databases, developer APIs, frontend web services and mobile apps






(Precedent: public genotype/phenotype data)

Accommodate multi-tier privacy standards
Ecosystem value propositions: service providers,
research community, biometric data-owners
Role of public and private service providers

October 28, 2013
QS Big Data

Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data
June 2013, 1(2): 85-99.

31
Github: de facto
QS Data
Commons

October 28, 2013
QS Big Data

Source: https://github.com/beaugunderson/genome

32
QS Frontier: Mental Performance Optimization
Mood Management Apps from
Mobilyze and M. Morris

PTSD App

‘Siri 2.0’ Personal Virtual Coach
from DIYgenomics

Source:
http://www.ptsd.va.gov/pu
blic/pages/ptsdcoach.asp
Sources: http://cbits.northwestern.edu and
http://quantifiedself.com/2009/03/a-few-weeks-ago-i

October 28, 2013
QS Big Data

Source: DIYgenomics Social Intelligence Study
http://diygenomics.pbworks.com/w/page/48946791/social_intelligence

33
Next-gen QS Services: Quality of Life
QS Aspiration Apps:
Happiness, Emotive
State (personal and
group), Well-being,
Goal Achievement

October 28, 2013
QS Big Data

Category and Name
Website URL
Happiness Tracking
Track Your Happiness
http://www.trackyourhappiness.org/
Mappiness
http://www.mappiness.org.uk/
The H(app)athon Project
http://www.happathon.com/
MoodPanda
http://moodpanda.com/
TechurSelf
http://www.techurself.com/urwell
Emotion Tracking and Sharing
Gotta Feeling
http://gottafeeling.com/
Emotish
http://emotish.com/
Feelytics
http://feelytics.me/
Expereal
http://expereal.com/
Population-level Emotion Barometers
We Feel Fine
http://wefeelfine.org/
moodmap
http://themoodmap.co.uk/
Pulse of the Nation
http://www.ccs.neu.edu/home/amislove/twittermood/
Twitter Mood Map
http://www.newscientist.com/blogs/onepercent/2011/09/twitt
er-reveals-the-worlds-emo-1.html
Wisdom 2.0
http://wisdom2summit.com/
Personal Wellbeing Platforms
GravityEight
http://www.gravityeight.com/
MindBloom
https://www.mindbloom.com/
Get Some Headspace
http://www.getsomeheadspace.com/
Curious
http://wearecurio.us/
uGooder
http://www.ugooder.com/
Goal Achievement Platforms
uMotif
http://www.uMotif.com/
DidThis
http://blog.didthis.com/
Schemer
https://www.schemer.com/ (personalized recommendations)
Pledge/Incentive-Based Goal Achievement Platforms
GymPact
http://www.gym-pact.com/
Stick
http://www.stickk.com/
Beeminder
https://www.beeminder.com/

Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June
2013, 1(2): 85-99.

34
Next-gen QS Services: Behavior Change

October 28, 2013
QS Big Data

Source: http://askmeevery.com/

35
Next-gen QS Services: Behavior Change




Shikake: Sensors embedded
in physical objects to trigger
a physical or psychological
behavior change
Examples:






Transparent trash cans
Trash cans playing an
appreciative sound to
encourage litter to be deposited
Stairs light up on approach
Appreciative ping/noise from
QS gadgetry

October 28, 2013
QS Big Data

Source: http://mtmr.jp/en/papers/taai2013v2.pdf

36
Next-gen QS Services: 3D Quantification
BodyMetrics and Poikos:
Fitness and Clothing
Customization Apps
OMsignal: Smart Apparel
24/7 Biometric Monitoring

October 28, 2013
QS Big Data

37
Continuous Information Climate


Fourth-person perspective: Immersed in infinite data
flow, we shed bits of information to the data flow, the
data flow responds by sending information to us

October 28, 2013
QS Big Data

Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June
2013, 1(2): 85-99.

38
Building Exosenses for the Qualified Self
Extending our senses in new ways to perceive data as sensation
Magnetic Sense: Finger and Arm Magnets

North Paw Haptic Compass Anklet and Heart Spark
http://www.youtube.com/watch?v=D4shfNufqSg
http://sensebridge.net/projects/heart-spark

October 28, 2013
QS Big Data

Serendipitous Joy: Smiletriggered EMG muscle sensor
with an LED headband display

Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified
Self 2.0. J Sens Actuator Netw 2012.

39
Exosenses as Quantified Intermediates








Networked quantified intermediates for
human senses: smarter, visible, sharable
through big data processing
Vague sense of heart rate variability, blood
pressure; haptically-available exosenses
make the data explicit
Haptics, audio, visual, taste, olfactory
mechanisms to make metrics explicit: heart
rate variability, blood pressure, galvanic skin
response, stress level
Skill as exosense: technology as memory,
self-experimentation as a form of exosense

October 28, 2013
QS Big Data

Source: web.mit.edu/newsoffice/2012/human-body-on-a-chip-research-funding-0724.html

Nose-on-a-chip

Gut-on-a-chip

Lung-on-a-chip
40
Neural Tracking: QS Big Data Frontier
24/7 Consumer EEG, Eye-tracking, Emotion-Mapping, Augmented Reality Glasses
Consumer EEG Rigs

Augmented Reality Glasses

1.0

2.0

October 28, 2013
QS Big Data

Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified Self 2.0. J Sens
Actuator Netw 2012.

41
QS Big Data: Biocitizen Volition
1. Continuous health information climate
Automated digital health monitoring, self-tracking devices,
and mobile apps providing personalized recommendations

2. Peer collaboration and
health advisors

Individual

Health social networks, crowdsourced
studies, health advisors, wellness
coaches, preventive care plans,
boutique physicians, genetics coaches,
aestheticians, medical tourism

3. Public health system
Deep expertise of traditional health system
for disease and trauma treatment
October 28, 2013
QS Big Data

Source: Extended from Swan, M. Emerging patient-driven health care models: an examination of health social networks, consumer
personalized medicine and quantified self-tracking. Int. J. Environ. Res. Public Health 2009, 2, 492-525.

42
Conceptualizing Big Data Categories
Personal Data

Group Data
October 28, 2013
QS Big Data

43
Agenda


Personal Data






Group Data




Quantified Self
Quantified Self and Big Data
Advanced QS Concepts
Urban Data

Conclusion

October 28, 2013
QS Big Data

44
Group Data: Smart City, Future City

October 28, 2013
QS Big Data

Image: http://www.sydmead.com

45
Global Population: Growing and Aging

October 28, 2013
QS Big Data

Source: UN Habitat – 2010
http://avondaleassetmanagement.blogspot.com/2012/05/japan-aging-population.html

46
3 billion new Internet users by 2020

October 28, 2013
QS Big Data

Source: Peter Diamandis Singularity University

47
Human Urbanization: Living in Cities




Over 50% worldwide population in 2008
5 billion in 2030 (estimated)
Megacity: (>10 million and possibly 2,000/km 2)

October 28, 2013
QS Big Data

48
Megacity
Growth
Rates

October 28, 2013
QS Big Data

Source: Wikipedia

49
Big Urban Data: Killer Apps




Adaptive lighting, smart waste, pest control, hygiene
management, eTolls, public transportation, traffic management,
smart grid, asset tracking, parking
Flexible services responding in real-time to individual and
community-level demand

October 28, 2013
QS Big Data

Source: MIT Senseable City Lab

50
Data Signature of Humanity
MIT SENSEable City Lab – the Real-Time City

October 28, 2013
QS Big Data

Source: http://senseable.mit.edu/signature-of-humanity/

51
3D Buildings + Population Density

October 28, 2013
QS Big Data

Source: ViziCities

52
3D Tweet Landscape

October 28, 2013
QS Big Data

Source: http://vimeo.com/67872925
http://www.slideshare.net/robhawkes/bringing-cities-to-life-using-big-data-webgl

53
3D Urban Data Viz: Decision-making Tool

October 28, 2013
QS Big Data

Source: http://www.wired.com/autopia/2013/08/london-underground-3d-map/

54
Group Data: Office Building Community

October 28, 2013
QS Big Data

Source: http://www.siembieda.com/burg.html, BURG, San Jose CA 2010

55
Big Data 3D Printed Dwellings of the Future

Living Treehouses – Mitchell Joachim

Masdar, Abu Dhabi – Energy City of the Future
October 28, 2013
QS Big Data

Himalayas Water Tower
Urban Agriculture: Vertical Farms

San Diego, California
(planned)
October 28, 2013
QS Big Data

Singapore (existing)
57
Reconfiguration of Space: Seasteading

October 28, 2013
QS Big Data
Transportation Revolution

Solar Power: Tesla + Solar City

Personalized Pod Transport
October 28, 2013
QS Big Data

Self-Driving Car

Source: Google's Self-Driving Cars Complete 300K Miles Without Accident, Deemed Ready for Commuting
http://techcrunch.com/2012/08/07/google-cars-300000-miles-without-accident/

59
Crowdsourcing

October 28, 2013
QS Big Data

Source: Eric Whitacre's Virtual Choir 3, 'Water Night' (2012), http://www.youtube.com/watch?v=V3rRaL-Czxw

60
Pervasiveness of Crowd Models


Crowdsourcing: coordination of large numbers of
individuals (the crowd) through an open call on the
Internet in the conduct of some sort of activity








Economics: crowdsourced labor marketplaces, crowdfunding,
grouppurchasing, data competition (Kaggle)
Politics: flashmobs, organizing, opinion-shifting, data-mining
Social: blogs, social networks, meetup, online dating
Art & Entertainment: virtual reality, multiplayer games
Education: MOOCs (massively open online courses)
Health: health social networks, digital health experimentation
communities, quantified self
Digital public goods: Wikipedia, online health databanks, data
commons resources, crowdscience competitions

October 28, 2013
QS Big Data

61
Genomera – Crowdsourced Study Platform

October 28, 2013
QS Big Data

Source: http://genomera.com/studies/dopamine-genes-and-rapid-realityadaptation-in-thinking

62
Agenda


Personal Data






Group Data




Quantified Self
Quantified Self and Big Data
Advanced QS Concepts
Urban Data

Conclusion

October 28, 2013
QS Big Data

63
But wait…Limitations and Risks



Transition to access not ownership models
Data rights and responsibilities




Regulatory and policy tensions







Personal data and group data
Surveillance (top-down) vs souveillance (bottom-up)
Multi-tier privacy and sharing preferences
Digital divide accessibility, non-discrimination

Precedent = Uninformed Consumer: Lack of access
conferred (e.g.; health data, genomics, credit scoring)
Consumer non-adoption, ease-of-use, social
acceptance, meaningful value propositions

October 28, 2013
QS Big Data

64
Proliferation of New QS Big Data Flows


QS Device Data






Personal IOT Data





Cell phone, wearable electronics data
Smartphone digital identity & payment

Personal Urban Data





Biometric data (HRM), personal genomic data
Personal medical and health data
QS neural-tracking eye-tracking affect data

Smart home, smart car
Smart city data (e.g.; transportation)

Personal Robotics Data

October 28, 2013
QS Big Data

65
Top 10 QS Big Data Trends
Personal Data

Group Data

QS Device Ecosystem
Internet-of-Things (IOT)
Sensor Networks
3D Information
Visualization

Wearable Electronics

Smart City
Future City

Megacity
Growth

Urban Data
October 28, 2013
QS Big Data

Biocitizen
Self-Empowerment
DIY Attitude

Crowdsourcing

3 billion New
People Online

66
Heidegger and Big Data


Technology is not good or bad in
itself, technology is an enabler, not a
means to an end (Kant: end not
means)



Our attunement to the background
of technology as a capacity for
revealing the world could help us
away from our lostness in daily
projects to see the possibilities for
the true meaningfulness of our being

October 28, 2013
QS Big Data

Source: Heidegger, M. The Question Concerning Technology, 1954

67
QS Big Data Summary


Next-gen QS services






IOT continuous personal information climates
QS Big Data





Wholly different relation to data: 99% noise
Rights and responsibilities model of data access

Group Data




Wearable Electronics as the QS platform
Improve quality of life, facilitate behavior change

Megacity growth, urban data flow, 3 bn coming online

Personal Data


Technology-enabled biocitizen-consumer takes action

October 28, 2013
QS Big Data

68
Big Data and the
Quantified Self
kittos!
Questions?

October 28, 2013
National Consumer Res Ctr, Helsinki, Finland
Slides: http://slideshare.net/LaBlogga

Melanie Swan
MS Futures Group
+1-650-681-9482
@LaBlogga, @DIYgenomics
www.MelanieSwan.com
m@melanieswan.com
http://www.youtube.com/TechnologyPhilosophe

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Big Data and the Quantified Self

  • 1. Big Data and the Quantified Self October 28, 2013 National Consumer Res Ctr, Helsinki, Finland Slides: http://slideshare.net/LaBlogga Melanie Swan MS Futures Group +1-650-681-9482 @LaBlogga, @DIYgenomics www.MelanieSwan.com m@melanieswan.com http://www.youtube.com/TechnologyPhilosophe
  • 2. About Melanie Swan      Founder DIYgenomics, science and technology innovator and philosopher Singularity University Instructor, IEET Affiliate Scholar, EDGE Contributor Education: MBA Finance, Wharton; BA French/Economics, Georgetown Univ Work experience: Fidelity, JP Morgan, iPass, RHK/Ovum, Arthur Andersen Sample publications:       Swan, M. Crowdsourced Health Research Studies: An Important Emerging Complement to Clinical Trials in the Public Health Research Ecosystem. J Med Internet Res 2012, Mar;14(2):e46. Swan, M. Scaling crowdsourced health studies: the emergence of a new form of contract research organization. Personalized Medicine 2012, Mar;9(2):223-234. Swan, M. Steady advance of stem cell therapies. Rejuvenation Res 2011, Dec;14(6):699-704. Swan, M., Hathaway, K., Hogg, C., McCauley, R., Vollrath, A. Citizen science genomics as a model for crowdsourced preventive medicine research. J Participat Med 2010, Dec 23; 2:e20. Swan, M. Multigenic Condition Risk Assessment in Direct-to-Consumer Genomic Services. Genet Med 2010, May;12(5):279-88. Swan, M. Emerging patient-driven health care models: an examination of health social networks, consumer personalized medicine and quantified self-tracking. Int J Environ Res Public Health 2009, 2, 492-525. October 28, 2013 QS Big Data Source: http://melanieswan.com/publications.htm 2
  • 3. Conceptualizing Big Data Categories Personal Data Tension: Individual vs Institution Group Data Sense of data belonging to a group October 28, 2013 QS Big Data 3
  • 4. Agenda  Personal Data     Group Data   Quantified Self Quantified Self and Big Data Advanced QS Concepts Urban Data Conclusion October 28, 2013 QS Big Data 4
  • 5. What is the Quantified Self?  Individual engaged in the selftracking of any kind of biological, physical, behavioral, or environmental information  Data acquisition through technology: wearable sensors, mobile apps, software interfaces, and online communities  Proactive stance: obtain and act on information October 28, 2013 QS Big Data Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99. 5
  • 6. QS Sensor Mania! Wearable Electronics Smartphone, Fitbit, Smartwatch (Pebble), Electronic T-shirt (Carre) Smartring (ElectricFoxy), Electronic tattoos (mc10), $1 blood API (Sano Intelligence), Continuous Monitors (Medtronic) October 28, 2013 QS Big Data Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified Self 2.0. J Sens Actuator Netw 2012. 6
  • 7. Wearable Personal Information Ecosystem Smart Gadgetry Creates Continuous Personal Information Climate New Wearable Categories: Smartwatch and AR/Glass Smartphone PC/Tablet/Cloud October 28, 2013 QS Big Data AR = Augmented Reality 7
  • 8. Next-gen Mini: BioSensor Electronic Tattoos Wearable Electronics: Detect External BioChemical Threats and Track Internal Vital Signs Electrochemical Sensors Chemical Sensors October 28, 2013 QS Big Data Disposable Electronics Source: http://www.jacobsschool.ucsd.edu/pulse/winter2013/page3.shtml#tattoos Tactile Intelligence: Haptic Data Glove 8
  • 9. Quantified Self Worldwide Community   Goal: personalized knowledge through quantified self-tracking ‘Show n tell’ meetups  What did you do? How did you do it? What did you learn? Videos, Conferences, Meetup Groups October 28, 2013 QS Big Data Source: Swan, M. Overview of Crowdsourced Health Research Studies. 2012. 9
  • 10. October 28, 2013 QS Big Data Source: http://www.meetup.com/Quantified-Self-Biohacking-Finland/ 10
  • 11. Quantified Self Project Examples  Food consumption (1 yr)1 and the Butter Mind study2 Study  Low-cost home-administered blood, urine, saliva tests  Cholestech LDX home cholesterol test October 28, 2013 QS Big Data 1 2 OrSense continuous non-invasive glucose monitoring Source: http://flowingdata.com/2011/06/29/a-year-of-food-consumption-visualized Source: http://quantifiedself.com/2011/01/results-of-the-buttermind-experiment ZRT Labs dried blood spot tests 11
  • 12. Quantified Self Measurements…  Physical Activities   Diet and Nutrition    Location, architecture, weather, noise, pollution, clutter, light, season Situational Variables   Mood, happiness, irritation, emotion, anxiety, esteem, depression, confidence IQ, alertness, focus, selective/sustained/divided attention, reaction, memory, verbal fluency, patience, creativity, reasoning, psychomotor vigilance Environmental Variables   Calories consumed, carbs, fat, protein, specific ingredients, glycemic index, satiety, portions, supplement doses, tastiness, cost, location Psychological, Mental, and Cognitive States and Traits   Miles, steps, calories, repetitions, sets, METs1 Context, situation, gratification of situation, time of day, day of week Social Variables  October 28, 2013 QS Big Data Influence, trust, charisma, karma, current role/status in the group or social network METs = Metabolic equivalents Source: http://measuredme.com/2012/10/building-thatperfect-quantified-self-app-notes-to-developers-and-qs-community-html/ 1 12
  • 13. The Quantified Self is Mainstream  Self-tracking statistics      60% US adults track weight, diet, or exercise 33% US adults monitor blood sugar, blood pressure, headaches, or sleep patterns 9% receive text message health alerts 40,000 smartphone health applications QS thought leadership    Press : BBC, Forbes, and Vanity Fair Electronics show focus at CES 2013 Health 2.0: “500+ companies making self-management tools; VC funding up 20%” October 28, 2013 QS Big Data Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99. 13
  • 14. Hype Curves per Google Trends 2011 October 28, 2013 QS Big Data 2013 2011 2013 14
  • 15. QS Experimentation Motivation and Features  DIYgenomics QS Study (n=37)  Desired outcome: optimality and improvement (vs pathology resolution)     Personalized intervention for depression, low energy, sleep quality, productivity, and cognitive alertness Rapid experimental iteration through solutions and kinds of solutions Resolution point found within weeks Pragmatic problem-solving focus, little introspection October 28, 2013 QS Big Data Source: DIYgenomics Knowledge Generation through Self-Experimentation Study http://genomera.com/studies/knowledge-generation-through-self-experimentation 15
  • 16. History of the Quantified Self   Sanctorius of Padua 16th c: energy expenditure in living systems; 30 years of QS weight/food data QS Philosophers    Epicureans, Heidegger, Foucault): ‘care of the self’ ‘Self’: recent concept of modernity QS: contemporary formalization using measurement, science, and technology to bring order and control to the natural world, including the human body October 28, 2013 QS Big Data Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99. 16
  • 17. Sensor Mania! October 28, 2013 QS Big Data Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified Self 2.0. J Sens Actuator Netw 2012. 17
  • 18. Wireless Internet-of-Things (IOT) Image credit: Cisco October 28, 2013 QS Big Data Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified Self 2.0. J Sens Actuator Netw 2012. 18
  • 19. 6 bn Current IOT devices to double by 2016 October 28, 2013 QS Big Data Source: http://www.businessinsider.com/growth-in-the-internet-of-things-2013-10?IR=T 19
  • 20. IOT World of Smart Matter  IOT Definition: digital networks of physical objects linked by the Internet that interact through web services  Usual gadgetry (e.g.; smartphones, tablets) and now everyday objects: cars, food, clothing, appliances, materials, parts, buildings, roads  Embedded microprocessors in 5% human-constructed objects (2012)1 October 28, 2013 QS Big Data Source: Vinge, V. Who’s Afraid of First Movers? The Singularity Summit 2012. http://singularitysummit.com/schedule 1 20
  • 21. IOT Contributing to Explosion of Big Data  Big Data: data sets too large and complex to process with on-hand database management tools  Examples    Walmart : 1 million transactions/hr transmitted to 3 PB database BBC: 7 PB video served/month from 100 PB physical disk space Structured and unstructured data (not pre-defined) October 28, 2013 QS Big Data Source: http://en.wikipedia.org/wiki/Big_data, http://wikibon.org/blog/big-data-statistics 21
  • 22. Defining Trend of Current Era: Big Data    Annual data creation on the order of zetabytes 90% of the world’s data created in the last 2 years Fastest growing segment: human biology-related data 2 year doubling cycle October 28, 2013 QS Big Data Source: Mary Meeker, Internet Trends, http://www.kpcb.com/insights/2013-internet-trends http://www.intel.com/content/dam/www/public/us/en/documents/white-papers/healthcare-leveraging-big-data-paper.pdf 22
  • 23. QS is inherently a Big Data problem   Data collection, processing, analysis Cloud computing for consumer processing    Local computing tools are not available to store, query, and manipulate QS data sets Cloud-based analysis: Predictive modeling, natural-language processing, machine learning algorithms over very-large data sets of heterogeneous data Rapid growth in QS data sets   Manually-tracked ‘small data’ is now automatically-collected ‘big data’ Examples: heart rate monitor data - 250 samples/second (9 GB/person/month); personal health ‘omics’ files October 28, 2013 QS Big Data Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99. 23
  • 24. QS Big Data: Personal Health ‘Omics’ DNA: SNP mutations DNA: Structural variation RNA expression profiling Health 2.0: Personal Health Informatics Proteomics Microbiomics Epigenetics Metabolomics October 28, 2013 QS Big Data Source: Academic papers re: integrated health data streams: Auffray C, et al. Looking back at genomic medicine in 2011. Genome Med. 2012 Jan 30;4(1):9. Chen R et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell. 2012 Mar 16;148(6):1293-307. 24
  • 25. Big Data: Integrated QS Data Streams Omics Data Streams Genome SNP mutations Structural variation Epigenetics Microbiome Traditional Data Streams Personal and Family Health History Proteome Self-reported data: health, exercise, food, mood journals, etc. Prescription History Transcriptome Metabolome Quantified Self Data Streams Mobile App Data Lab Tests: History and Current Demographic Data Quantified Self Device Data Standardized Instrument Response Biosensor Data Objective Metrics Diseasome Environmentome October 28, 2013 QS Big Data Swan, M. Health 2050: The Realization of Personalized Medicine through Crowdsourcing, the Quantified Self, and the Participatory Biocitizen. J Pers Med 2012, 2(3), 93-118. Legend: Consumer-available 25
  • 26. APIs and Multi-QS Data Stream Integration October 28, 2013 QS Big Data 26
  • 27. Fluxstream Unified QS Dashboard October 28, 2013 QS Big Data Source: http://johnfass.wordpress.com/2012/09/06/bodytrackfluxtream/ 27
  • 28. Sen.se Integrated QS Dashboard  ‘Mulitviz’ display: investigate correlation between coffee consumption, social interaction, and mood October 28, 2013 QS Big Data Source: http://blog.sen.se/post/19174708614/mashups-turning-your-data-intosomething-useable-and 28
  • 29. Wholly different concept and relation to data   Formerly everything signal, now 99% noise Medium of big data opens up new methods:  Exception, characterization, variability, pattern recognition, correlation, prediction, early warnings  Allows attitudinal shift to active from reactive  Two-way communication: translate biometric variability in the personal informatics climate to real-time recommendations Example: degradation in sleep quality and hemoglobin A1C levels predict diabetes onset by 10 years1  October 28, 2013 QS Big Data Source: Heianza et al. High normal HbA(1c) levels were associated with impaired insulin secretion. Diabet Med 2012. 29:1285-1290. 1 29
  • 30. Big Data opens up new Methods   Google: large corpora and simple algorithms Foundational characterization (previously unavailable)  Longitudinal baseline measures of internal and external daily rhythms, normal deviation patterns, contingency adjustments, anomaly, and emergent phenomena  New kinds of Pattern Recognition (different structures)  Analyze data in multiple paradigms: time, frequency, episode, cycle, and systemic variables New trends, cyclicality, episodic triggers, and other elements that are not clear in traditional time-linear data   Multi-disciplinarity  Turbulence, topology, chaos, complexity, etc. models October 28, 2013 QS Big Data Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99. 30
  • 31. Opportunity: QS Data Commons  Common repository for personal informatics data streams   Fitbit, Jawbone UP, Nike, Withings, myZeo, 23andMe, Glass, Pebble, Basis, BodyMedia Architecting consumer-friendly models  Open-access databases, developer APIs, frontend web services and mobile apps     (Precedent: public genotype/phenotype data) Accommodate multi-tier privacy standards Ecosystem value propositions: service providers, research community, biometric data-owners Role of public and private service providers October 28, 2013 QS Big Data Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99. 31
  • 32. Github: de facto QS Data Commons October 28, 2013 QS Big Data Source: https://github.com/beaugunderson/genome 32
  • 33. QS Frontier: Mental Performance Optimization Mood Management Apps from Mobilyze and M. Morris PTSD App ‘Siri 2.0’ Personal Virtual Coach from DIYgenomics Source: http://www.ptsd.va.gov/pu blic/pages/ptsdcoach.asp Sources: http://cbits.northwestern.edu and http://quantifiedself.com/2009/03/a-few-weeks-ago-i October 28, 2013 QS Big Data Source: DIYgenomics Social Intelligence Study http://diygenomics.pbworks.com/w/page/48946791/social_intelligence 33
  • 34. Next-gen QS Services: Quality of Life QS Aspiration Apps: Happiness, Emotive State (personal and group), Well-being, Goal Achievement October 28, 2013 QS Big Data Category and Name Website URL Happiness Tracking Track Your Happiness http://www.trackyourhappiness.org/ Mappiness http://www.mappiness.org.uk/ The H(app)athon Project http://www.happathon.com/ MoodPanda http://moodpanda.com/ TechurSelf http://www.techurself.com/urwell Emotion Tracking and Sharing Gotta Feeling http://gottafeeling.com/ Emotish http://emotish.com/ Feelytics http://feelytics.me/ Expereal http://expereal.com/ Population-level Emotion Barometers We Feel Fine http://wefeelfine.org/ moodmap http://themoodmap.co.uk/ Pulse of the Nation http://www.ccs.neu.edu/home/amislove/twittermood/ Twitter Mood Map http://www.newscientist.com/blogs/onepercent/2011/09/twitt er-reveals-the-worlds-emo-1.html Wisdom 2.0 http://wisdom2summit.com/ Personal Wellbeing Platforms GravityEight http://www.gravityeight.com/ MindBloom https://www.mindbloom.com/ Get Some Headspace http://www.getsomeheadspace.com/ Curious http://wearecurio.us/ uGooder http://www.ugooder.com/ Goal Achievement Platforms uMotif http://www.uMotif.com/ DidThis http://blog.didthis.com/ Schemer https://www.schemer.com/ (personalized recommendations) Pledge/Incentive-Based Goal Achievement Platforms GymPact http://www.gym-pact.com/ Stick http://www.stickk.com/ Beeminder https://www.beeminder.com/ Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99. 34
  • 35. Next-gen QS Services: Behavior Change October 28, 2013 QS Big Data Source: http://askmeevery.com/ 35
  • 36. Next-gen QS Services: Behavior Change   Shikake: Sensors embedded in physical objects to trigger a physical or psychological behavior change Examples:     Transparent trash cans Trash cans playing an appreciative sound to encourage litter to be deposited Stairs light up on approach Appreciative ping/noise from QS gadgetry October 28, 2013 QS Big Data Source: http://mtmr.jp/en/papers/taai2013v2.pdf 36
  • 37. Next-gen QS Services: 3D Quantification BodyMetrics and Poikos: Fitness and Clothing Customization Apps OMsignal: Smart Apparel 24/7 Biometric Monitoring October 28, 2013 QS Big Data 37
  • 38. Continuous Information Climate  Fourth-person perspective: Immersed in infinite data flow, we shed bits of information to the data flow, the data flow responds by sending information to us October 28, 2013 QS Big Data Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99. 38
  • 39. Building Exosenses for the Qualified Self Extending our senses in new ways to perceive data as sensation Magnetic Sense: Finger and Arm Magnets North Paw Haptic Compass Anklet and Heart Spark http://www.youtube.com/watch?v=D4shfNufqSg http://sensebridge.net/projects/heart-spark October 28, 2013 QS Big Data Serendipitous Joy: Smiletriggered EMG muscle sensor with an LED headband display Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified Self 2.0. J Sens Actuator Netw 2012. 39
  • 40. Exosenses as Quantified Intermediates     Networked quantified intermediates for human senses: smarter, visible, sharable through big data processing Vague sense of heart rate variability, blood pressure; haptically-available exosenses make the data explicit Haptics, audio, visual, taste, olfactory mechanisms to make metrics explicit: heart rate variability, blood pressure, galvanic skin response, stress level Skill as exosense: technology as memory, self-experimentation as a form of exosense October 28, 2013 QS Big Data Source: web.mit.edu/newsoffice/2012/human-body-on-a-chip-research-funding-0724.html Nose-on-a-chip Gut-on-a-chip Lung-on-a-chip 40
  • 41. Neural Tracking: QS Big Data Frontier 24/7 Consumer EEG, Eye-tracking, Emotion-Mapping, Augmented Reality Glasses Consumer EEG Rigs Augmented Reality Glasses 1.0 2.0 October 28, 2013 QS Big Data Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified Self 2.0. J Sens Actuator Netw 2012. 41
  • 42. QS Big Data: Biocitizen Volition 1. Continuous health information climate Automated digital health monitoring, self-tracking devices, and mobile apps providing personalized recommendations 2. Peer collaboration and health advisors Individual Health social networks, crowdsourced studies, health advisors, wellness coaches, preventive care plans, boutique physicians, genetics coaches, aestheticians, medical tourism 3. Public health system Deep expertise of traditional health system for disease and trauma treatment October 28, 2013 QS Big Data Source: Extended from Swan, M. Emerging patient-driven health care models: an examination of health social networks, consumer personalized medicine and quantified self-tracking. Int. J. Environ. Res. Public Health 2009, 2, 492-525. 42
  • 43. Conceptualizing Big Data Categories Personal Data Group Data October 28, 2013 QS Big Data 43
  • 44. Agenda  Personal Data     Group Data   Quantified Self Quantified Self and Big Data Advanced QS Concepts Urban Data Conclusion October 28, 2013 QS Big Data 44
  • 45. Group Data: Smart City, Future City October 28, 2013 QS Big Data Image: http://www.sydmead.com 45
  • 46. Global Population: Growing and Aging October 28, 2013 QS Big Data Source: UN Habitat – 2010 http://avondaleassetmanagement.blogspot.com/2012/05/japan-aging-population.html 46
  • 47. 3 billion new Internet users by 2020 October 28, 2013 QS Big Data Source: Peter Diamandis Singularity University 47
  • 48. Human Urbanization: Living in Cities    Over 50% worldwide population in 2008 5 billion in 2030 (estimated) Megacity: (>10 million and possibly 2,000/km 2) October 28, 2013 QS Big Data 48
  • 49. Megacity Growth Rates October 28, 2013 QS Big Data Source: Wikipedia 49
  • 50. Big Urban Data: Killer Apps   Adaptive lighting, smart waste, pest control, hygiene management, eTolls, public transportation, traffic management, smart grid, asset tracking, parking Flexible services responding in real-time to individual and community-level demand October 28, 2013 QS Big Data Source: MIT Senseable City Lab 50
  • 51. Data Signature of Humanity MIT SENSEable City Lab – the Real-Time City October 28, 2013 QS Big Data Source: http://senseable.mit.edu/signature-of-humanity/ 51
  • 52. 3D Buildings + Population Density October 28, 2013 QS Big Data Source: ViziCities 52
  • 53. 3D Tweet Landscape October 28, 2013 QS Big Data Source: http://vimeo.com/67872925 http://www.slideshare.net/robhawkes/bringing-cities-to-life-using-big-data-webgl 53
  • 54. 3D Urban Data Viz: Decision-making Tool October 28, 2013 QS Big Data Source: http://www.wired.com/autopia/2013/08/london-underground-3d-map/ 54
  • 55. Group Data: Office Building Community October 28, 2013 QS Big Data Source: http://www.siembieda.com/burg.html, BURG, San Jose CA 2010 55
  • 56. Big Data 3D Printed Dwellings of the Future Living Treehouses – Mitchell Joachim Masdar, Abu Dhabi – Energy City of the Future October 28, 2013 QS Big Data Himalayas Water Tower
  • 57. Urban Agriculture: Vertical Farms San Diego, California (planned) October 28, 2013 QS Big Data Singapore (existing) 57
  • 58. Reconfiguration of Space: Seasteading October 28, 2013 QS Big Data
  • 59. Transportation Revolution Solar Power: Tesla + Solar City Personalized Pod Transport October 28, 2013 QS Big Data Self-Driving Car Source: Google's Self-Driving Cars Complete 300K Miles Without Accident, Deemed Ready for Commuting http://techcrunch.com/2012/08/07/google-cars-300000-miles-without-accident/ 59
  • 60. Crowdsourcing October 28, 2013 QS Big Data Source: Eric Whitacre's Virtual Choir 3, 'Water Night' (2012), http://www.youtube.com/watch?v=V3rRaL-Czxw 60
  • 61. Pervasiveness of Crowd Models  Crowdsourcing: coordination of large numbers of individuals (the crowd) through an open call on the Internet in the conduct of some sort of activity        Economics: crowdsourced labor marketplaces, crowdfunding, grouppurchasing, data competition (Kaggle) Politics: flashmobs, organizing, opinion-shifting, data-mining Social: blogs, social networks, meetup, online dating Art & Entertainment: virtual reality, multiplayer games Education: MOOCs (massively open online courses) Health: health social networks, digital health experimentation communities, quantified self Digital public goods: Wikipedia, online health databanks, data commons resources, crowdscience competitions October 28, 2013 QS Big Data 61
  • 62. Genomera – Crowdsourced Study Platform October 28, 2013 QS Big Data Source: http://genomera.com/studies/dopamine-genes-and-rapid-realityadaptation-in-thinking 62
  • 63. Agenda  Personal Data     Group Data   Quantified Self Quantified Self and Big Data Advanced QS Concepts Urban Data Conclusion October 28, 2013 QS Big Data 63
  • 64. But wait…Limitations and Risks   Transition to access not ownership models Data rights and responsibilities   Regulatory and policy tensions      Personal data and group data Surveillance (top-down) vs souveillance (bottom-up) Multi-tier privacy and sharing preferences Digital divide accessibility, non-discrimination Precedent = Uninformed Consumer: Lack of access conferred (e.g.; health data, genomics, credit scoring) Consumer non-adoption, ease-of-use, social acceptance, meaningful value propositions October 28, 2013 QS Big Data 64
  • 65. Proliferation of New QS Big Data Flows  QS Device Data     Personal IOT Data    Cell phone, wearable electronics data Smartphone digital identity & payment Personal Urban Data    Biometric data (HRM), personal genomic data Personal medical and health data QS neural-tracking eye-tracking affect data Smart home, smart car Smart city data (e.g.; transportation) Personal Robotics Data October 28, 2013 QS Big Data 65
  • 66. Top 10 QS Big Data Trends Personal Data Group Data QS Device Ecosystem Internet-of-Things (IOT) Sensor Networks 3D Information Visualization Wearable Electronics Smart City Future City Megacity Growth Urban Data October 28, 2013 QS Big Data Biocitizen Self-Empowerment DIY Attitude Crowdsourcing 3 billion New People Online 66
  • 67. Heidegger and Big Data  Technology is not good or bad in itself, technology is an enabler, not a means to an end (Kant: end not means)  Our attunement to the background of technology as a capacity for revealing the world could help us away from our lostness in daily projects to see the possibilities for the true meaningfulness of our being October 28, 2013 QS Big Data Source: Heidegger, M. The Question Concerning Technology, 1954 67
  • 68. QS Big Data Summary  Next-gen QS services     IOT continuous personal information climates QS Big Data    Wholly different relation to data: 99% noise Rights and responsibilities model of data access Group Data   Wearable Electronics as the QS platform Improve quality of life, facilitate behavior change Megacity growth, urban data flow, 3 bn coming online Personal Data  Technology-enabled biocitizen-consumer takes action October 28, 2013 QS Big Data 68
  • 69. Big Data and the Quantified Self kittos! Questions? October 28, 2013 National Consumer Res Ctr, Helsinki, Finland Slides: http://slideshare.net/LaBlogga Melanie Swan MS Futures Group +1-650-681-9482 @LaBlogga, @DIYgenomics www.MelanieSwan.com m@melanieswan.com http://www.youtube.com/TechnologyPhilosophe